from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2022-12-13 14:02:55.918353
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 13, Dec, 2022
Time: 14:03:02
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -51.2048
Nobs: 869.000 HQIC: -51.5096
Log likelihood: 11455.5 FPE: 3.52868e-23
AIC: -51.6985 Det(Omega_mle): 3.18340e-23
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.295541 0.049882 5.925 0.000
L1.Burgenland 0.106024 0.034134 3.106 0.002
L1.Kärnten -0.106997 0.018322 -5.840 0.000
L1.Niederösterreich 0.215227 0.071649 3.004 0.003
L1.Oberösterreich 0.087581 0.067897 1.290 0.197
L1.Salzburg 0.250085 0.036233 6.902 0.000
L1.Steiermark 0.030054 0.047589 0.632 0.528
L1.Tirol 0.127942 0.038763 3.301 0.001
L1.Vorarlberg -0.062732 0.033282 -1.885 0.059
L1.Wien 0.061449 0.060694 1.012 0.311
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.063930 0.102663 0.623 0.533
L1.Burgenland -0.009365 0.070251 -0.133 0.894
L1.Kärnten 0.049123 0.037709 1.303 0.193
L1.Niederösterreich -0.174129 0.147463 -1.181 0.238
L1.Oberösterreich 0.365117 0.139740 2.613 0.009
L1.Salzburg 0.286404 0.074571 3.841 0.000
L1.Steiermark 0.108729 0.097943 1.110 0.267
L1.Tirol 0.318157 0.079779 3.988 0.000
L1.Vorarlberg 0.024079 0.068498 0.352 0.725
L1.Wien -0.025937 0.124915 -0.208 0.836
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.199232 0.025829 7.713 0.000
L1.Burgenland 0.090214 0.017675 5.104 0.000
L1.Kärnten -0.009122 0.009487 -0.962 0.336
L1.Niederösterreich 0.267415 0.037101 7.208 0.000
L1.Oberösterreich 0.113938 0.035158 3.241 0.001
L1.Salzburg 0.052848 0.018762 2.817 0.005
L1.Steiermark 0.015840 0.024642 0.643 0.520
L1.Tirol 0.101622 0.020072 5.063 0.000
L1.Vorarlberg 0.056127 0.017234 3.257 0.001
L1.Wien 0.112949 0.031428 3.594 0.000
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.105166 0.026508 3.967 0.000
L1.Burgenland 0.047705 0.018139 2.630 0.009
L1.Kärnten -0.016987 0.009737 -1.745 0.081
L1.Niederösterreich 0.197081 0.038076 5.176 0.000
L1.Oberösterreich 0.278418 0.036082 7.716 0.000
L1.Salzburg 0.118150 0.019255 6.136 0.000
L1.Steiermark 0.100165 0.025290 3.961 0.000
L1.Tirol 0.126783 0.020599 6.155 0.000
L1.Vorarlberg 0.069650 0.017687 3.938 0.000
L1.Wien -0.026938 0.032254 -0.835 0.404
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.131593 0.047928 2.746 0.006
L1.Burgenland -0.053622 0.032797 -1.635 0.102
L1.Kärnten -0.037297 0.017604 -2.119 0.034
L1.Niederösterreich 0.167315 0.068843 2.430 0.015
L1.Oberösterreich 0.131990 0.065238 2.023 0.043
L1.Salzburg 0.291300 0.034814 8.367 0.000
L1.Steiermark 0.034436 0.045725 0.753 0.451
L1.Tirol 0.162681 0.037245 4.368 0.000
L1.Vorarlberg 0.107271 0.031979 3.354 0.001
L1.Wien 0.065908 0.058317 1.130 0.258
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.059854 0.037970 1.576 0.115
L1.Burgenland 0.038489 0.025983 1.481 0.139
L1.Kärnten 0.049816 0.013947 3.572 0.000
L1.Niederösterreich 0.227562 0.054540 4.172 0.000
L1.Oberösterreich 0.271221 0.051684 5.248 0.000
L1.Salzburg 0.058970 0.027581 2.138 0.033
L1.Steiermark -0.007115 0.036225 -0.196 0.844
L1.Tirol 0.157604 0.029507 5.341 0.000
L1.Vorarlberg 0.068794 0.025335 2.715 0.007
L1.Wien 0.075459 0.046201 1.633 0.102
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.185091 0.045512 4.067 0.000
L1.Burgenland 0.019083 0.031143 0.613 0.540
L1.Kärnten -0.060360 0.016717 -3.611 0.000
L1.Niederösterreich -0.093994 0.065372 -1.438 0.150
L1.Oberösterreich 0.178205 0.061949 2.877 0.004
L1.Salzburg 0.060889 0.033059 1.842 0.065
L1.Steiermark 0.229644 0.043420 5.289 0.000
L1.Tirol 0.488479 0.035367 13.812 0.000
L1.Vorarlberg 0.050306 0.030366 1.657 0.098
L1.Wien -0.055436 0.055377 -1.001 0.317
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.157961 0.051741 3.053 0.002
L1.Burgenland 0.000042 0.035406 0.001 0.999
L1.Kärnten 0.066389 0.019005 3.493 0.000
L1.Niederösterreich 0.200743 0.074321 2.701 0.007
L1.Oberösterreich -0.069923 0.070428 -0.993 0.321
L1.Salzburg 0.220270 0.037584 5.861 0.000
L1.Steiermark 0.112706 0.049363 2.283 0.022
L1.Tirol 0.084107 0.040208 2.092 0.036
L1.Vorarlberg 0.123405 0.034523 3.575 0.000
L1.Wien 0.106054 0.062957 1.685 0.092
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.358171 0.030534 11.730 0.000
L1.Burgenland 0.006633 0.020894 0.317 0.751
L1.Kärnten -0.025282 0.011215 -2.254 0.024
L1.Niederösterreich 0.228983 0.043859 5.221 0.000
L1.Oberösterreich 0.155696 0.041562 3.746 0.000
L1.Salzburg 0.053021 0.022179 2.391 0.017
L1.Steiermark -0.016107 0.029130 -0.553 0.580
L1.Tirol 0.121013 0.023728 5.100 0.000
L1.Vorarlberg 0.071280 0.020373 3.499 0.000
L1.Wien 0.048054 0.037152 1.293 0.196
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.038227 0.158375 0.180224 0.167759 0.140862 0.125859 0.064706 0.220011
Kärnten 0.038227 1.000000 0.000598 0.131438 0.026673 0.098919 0.432457 -0.049370 0.101471
Niederösterreich 0.158375 0.000598 1.000000 0.344855 0.169453 0.311703 0.125736 0.191131 0.341865
Oberösterreich 0.180224 0.131438 0.344855 1.000000 0.233702 0.341278 0.176771 0.179975 0.273372
Salzburg 0.167759 0.026673 0.169453 0.233702 1.000000 0.152736 0.136471 0.152432 0.141497
Steiermark 0.140862 0.098919 0.311703 0.341278 0.152736 1.000000 0.158798 0.147600 0.094251
Tirol 0.125859 0.432457 0.125736 0.176771 0.136471 0.158798 1.000000 0.122436 0.165937
Vorarlberg 0.064706 -0.049370 0.191131 0.179975 0.152432 0.147600 0.122436 1.000000 0.019901
Wien 0.220011 0.101471 0.341865 0.273372 0.141497 0.094251 0.165937 0.019901 1.000000